Cooperative Cognitive Network: Performance

Analysis of Cyclostationary Spectrum Detection

Rafaqat Ali1, Aftab Ahmad, Babar Hussain, Nagina Zarin, Imran Khan2, Iftikhar Ahmed Khan

Department of Telecommunication Engineering

University of Engineering & Technology Peshawar

Khyber Pukhtoonkhwa, Pakistan

1rali_kk@yahoo.com,

2imrankhan@nwfpuet.edu.pk

Abstract The Cognitive Radio (CR) continuously monitors

the spectrum to detect the presence or absence of the primary

user. Cyclostationary is one the efficient spectrum sensing

method as it can differentiate among signal, interference and

noise in low SNR. In this paper we illustrate the cooperation in

cognitive radios by using the Cyclostationary sensing method. We

consider the case of cognitive radio cooperating through AWGN

channel and using both Amplify-and-Forward (AF) and decode-

and-forward (DF) Relaying schemes with different relay

locations and exploit their probability of detection. Here we

generalize our analysis to two-relay based cognitive radio

network for Monte Carlo simulation.

Index Terms Cognitive Radio, Cyclostationary detection,

Relays, Probability of Detection, Probability of false alarm.

I. INTRODUCTION

With the marked and wide spread development of the mobile

telecommunication industry, the rapidly growing demand for

radio spectrum is becoming a serious issue. Frequency

spectrum which is a scarce resource for wireless

communications is most likely used by diverse users and

applications in the next generation Wireless networks. The

spectrum utilization varies significantly with time and

location. [1]- [7]

In such a situation, the incompetent use of limited frequency

spectrum can be effectively improved through the use of

cognitive radio (CR) technology, which is deemed to enable

wireless devices to utilize the spectrum with adaption and

efficiency [2]- wireless technology

that can be used to sense, recognize and utilize the unused

radio spectrum sensibly at a given time [4]. The new radio

spectrum users through this technology are named as CR users

[5]. These CR users use the spectrum as long as the licensed

user, users who are endowed with higher priority to utilize a

particular spectrum, is idle and have to quit the channel

quickly and promptly soon after the licensed user reappears

[6].

The core issue of a cognitive radio is sensing, the ability to

detect the presence or absence of the Primary User. How can

this job be accomplished is of major concern in the

deployment of Cognitive Radio.

When it comes to options in choosing the better sensing

technique, tempting options exist in form of a multitude of

techniques. Various sensing techniques are used: i.e.

Cyclostationary detection, Energy detection and Matched

filter [9]-[11], sensing the primary user and white spaces.

Cyclostationary detection, while having computation

complications and spectral leakage of high amplitude signals,

is one of the efficient sensing techniques showing better

performance at low SNR [11]-[12]. Additionally, it has the

capability to learn about the modulation type being used at

transmitter side [20]. In comparison with other sensing

technique, this method is less susceptible to noise, especially

in strong adjacent channel interference, as discrimination is

based on periodic properties of signal [20].

signal in a spectrum have some non-random components that

can be exploit by Cognitive Radio to discern it from noise.

These features include carrier frequency, symbol period,

modulation type and chipping rate [8]. A wireless signal is

cyclostationary as its Mean and Autocorrelation function

exhibit periodicity [7-9].

To safeguard the primary user from undue interference, we

have to tackle with different issues like hidden node problem,

multipath fading and shadowing. Cooperative spectrum

sensing so far has been proved favourable in mitigating these

effects along with cooperative gain [20]. To improve the

sensing capabilities a cooperation protocol is needed which is

amplify-and-forward (AF) relaying scheme, which leads to

increase the detection probability [13].

In this paper, the performance of cooperative cognitive

network is analyzed in terms of probability of detection and

probability of false Alarm. The multiple relays are operating

in Amplify-and-forward (AF) mode with variable gain, and

are located at different positions within the communication

range of PU and destination. The cognitive coordinator use

Cyclostationary spectrum detection. Monte Carlo simulation is

performed showing the performance curves for various relays

locations. Channel state information (ISI) is assumed to be

available at the relays for primary user to relay links and at the

cognitive coordinator for relay to cognitive coordinator link.

978-1-4673-4451-7/12/$31.00 ©2012 IEEE

At the cognitive coordinator, the received signals are

combined using maximal ratio combining (MRC).

II. SYSTEM MODEL

Cognitive radio network is analyzed using Cyclostationary

sensing technique with multiple relays operating in Amplify-

and-Forward (AF) relaying scheme. The system model

consists of primary user (PU), number of relays (

through , where ), Cognitive coordinator (SU)

as shown in fig. 1. The relays are assumed at different

locations from PU and SU, transmitting through AWGN

channel. In this scenario below, represents the distance

from PU to -th relay and represents the distance from -th

relay to SU.

Figure 1.System Model

Relay optimization are used to find out two assumptions i.e.

whether the primary user is present (Hypothesis ) or

absent (Hypothesis ). The probability of detection (Pd)

and probability of false alarm (Pf) at different values of SNR

are plotted using relay optimization.

III. TRANSMISSION PROTOCOL

The TDMA-based two timeslots protocol is used for

transmission. In 1st timeslot, source (PU) transmits a signal to

the relays while in 2nd

time slot the relays, operating in AF

mode, forward the amplified signal to the destination (SU).

Assume the signal received at the -th relay from the primary

user is described as:

Where, represents the primary user signal, is the

noise signal (AWGN), and is the path loss exponent

( for urban area).

The signal is then amplified at -th relay using Amplify-and-

forward scheme as:

In 2nd timeslot, the relays transmit the amplified signal, ,

to the destination (SU) and can be represent as:

At cognitive coordinator, different signals received from

multiple relays are combined using maximal ratio combining

(MRC). Detection technique is than applied on the combined

signal to perceive one of the two hypotheses.

IV. CYCLOSTATIONARY SPECTRUM DETECTION

Cyclostationary is one of the efficient sensing technique

having 90% of probability of detection and 10 % of

probability of false alarm having SNR -8db or more [7]. The

signal received at the destination (SU) contains some features

which exhibit periodicity. Carrier frequency, symbol period,

modulation type and chipping rate are the features which are

detected by the CR to discriminate the signal from noise.

Mean and Autocorrelation of a signal also have (exhibit)

periodicity [7-9]. Detecting these properties means detecting

the PU in the spectrum. Noise signal is static i.e. non-periodic,

so it can easily be differentiated from the signal.

Suppose the signal received at the cognitive radio is . In

[11] - [8] The Autocorrelation and Mean of the received signal

at the CR have been expressed.

The Mean of the signal can be represented as [2]:

Where is period for time .

While autocorrelation function, , of the signal is

expressed in [2] as:

Rm

SU PU

R2

R1

Where is periodic and can be expressed in Fourier

series as [18]:

Fourier transform of Fourier coefficient, called cyclic

autocorrelation, results in Spectral Correlation Function (SCF)

for defined number of samples, expressed as [18]:

Autocorrelation and SCF results in a peak of detection for

signal presence or absence as shown in the fig 2.

Figure 2.Spectral Correlation Function

Now the decision has to be made between two possible

hypotheses i.e. either the signal is present or it is absent, and

can be represent respectively as:

(8)

Here is the threshold for decision. For the specified value

of , can be obtained as [18]:

(9)

Also, the probability of false alarm can be expressed as [18]:

Where is the complementary cumulative function of

standard Gauss signal.

Finally, the probability of detection can be expressed as

[19]:

V. RESULTS AND DISCUSSIONS

For simplicity we have assumed two relays and ,

located at distance and respectively from the source

(PU) and at distance and respectively from the

destination (SU). For the total distance between PU and SU

normalized to 1 (i.e. ), both the relays are supposed at

position distance apart from the straight communication

line joining PU and SU. The projections distances, and ,

are chosen

PU as shown in fig. 3.

The signals received after passing through two relays

and are and respectively and are eventually

added using maximal ratio combining (MRC) for further

processing.

Figure 3. Simulation Model

SU

PU

A. ROC ANALYSIS

The Receiver Characteristic Curve (ROC), which relates Pd

with Pf, is estimation for performance analysis of the detection

technique used. It has a direct relation with the sample points

(L) and SNR. An increase in the sample points (L) and SNR

will increase the probability of detection and hence improves

ROC curve. For different value of sample points and SNR the

Pd vs Pf for the proposed technique is shown in the fig 4. Both

the relays are considered at position halfway from PU as well

as from SU.

Figure 4. Pd vs Pf for different Sample Points and SNR

B. PROBABILITY DETECTION AND SNR

Probability of detection (Pd) is another estimation for

performance analysis of a detection technique the low level of

which shows higher vulnerability of PU to interference. For a

scenario with low SNR value, i.e. high noise level, probability

of detection tends to decrease.

The Probability of detection vs SNR for different location of

the relays is shown in fig 5.

Table 1 Position of R1 and R2

Position 1 2

( 0.2, 0.3) (0.5, 0.4)

Table 1 shows the position of the relays, Where are

the projection distances of the relay one and relay two

respectively from the PU as shown in fig 3. Fig 5 shows that

the Pd increases with the increase of SNR, and also that Pd for

relay link is highly dependent on the locality of relays.

Figure 5. Pd vs SNR at different location of Relays

C. PROBABILITY OF DETECTION

Fig 6 shows the relation of the probability of detection of the

BPSK signal using relay optimization. Probability of detection

is plotted against projection distances of relays

respectively from PU. The fig 6 shows that position

of relays significantly effect Pd, and has highest value when at

least one of the relay approach the SU.

Figure 6. Pd vs d1 vs d2

VI. CONCLUSION

The cooperation in cognitive radios by using the

Cyclostationary sensing method has been illustrated. We

consider the case of cognitive radio cooperating through

AWGN channel and using Amplify-and-Forward (AF)

Relaying scheme with different relay locations and exploit

their probability of detection. Here we generalize our analysis

to two-relay based cognitive radio network for Monte Carlo

simulation. It is shown that the maximum probability of

detection is obtained when at least one of the communicating

relays are located near the destination.

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